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on integrating an agentic platform for electrocatalyst discovery and developing novel machine-learning algorithms (e.g., reinforcement learning) for materials discovery and process optimization. The candidate will
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optimisation, algorithm design and high-performance computing, with application to airport innovation. Successful candidates will join an active group of Principal Investigators and researchers to work within
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cancers (Dentro et al., Cell 2021), the evolutionary history of cancer (Gerstung et al., Nature 2020), biallelic mutations in cancer genomes (Demeulemeester et al., Nature Genetics 2022), combined DNA
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language models, multimodal generative AI, and related areas. Develop algorithms, architectures, and training methods for world models, LLMs, and multimodal AI systems. Design methods for prediction
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collaboration that uses AI algorithms to recommend and coordinate experiments across distributed laboratories. Your work will focus on the synthesis and characterisation of AI-recommended conjugated materials
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developing/adapting computational models or algorithms to analyse biological data, and experience with single cell or spatial omics datasets or knowledge of cancer biology would be an advantage. What we offer
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system model new module integration, scenario simulations, and prognostics analyses Physics-informed deep learning/hybrid modeling/reasoning AI algorithm development and optimization Job Requirements: A
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framework to find the optimal operation strategy Conduct computer programming to verify the efficiency of the designed solution algorithms Analyze data acquired from the field survey Develop machine learning
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algorithms for: Fine-grained video understanding Concept learning Temporal reasoning Vision-Language Models (VLMs) Multimodal representation learning Self-supervised and weakly supervised learning Fine-tune
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settings. Develop and test algorithms for object detection, tracking, and classification using wireless sensors. Help guide and mentor graduate students and other junior team members working on the project